Abstract
The early detection of skin cancer can lead to high prognosis rate. Thus it is very important to identify abnormalities in skin as early as possible. However, the detection of abnormalities at their early stages is a challenging task since the shape and colour of the abnormalities vary with different persons. In this study, fractal model for skin cancer diagnosis is developed. Differential Box Counting (DBC) method is implemented to get the fractal dimension from the dermoscopic images from two databases; International Skin Imaging Collaboration (ISIC) and PH2 database. The fractal features are classified using a parametric and non-parametric classification approach. The system provides promising results for skin cancer diagnosis with 96.5% accuracy on PH2 images and 91.5% accuracy on ISIC database images using the non-parametric classifier whereas parametric classifier gives 95% (PH2) and 90% (ISIC) images.
Highlights
IntroductionAs the key organ guarding our internal tissues from outside intrusions, it is not rare tat skin itself is suffering from demagogical problems
Human skin comprises the largest share of weight of the human body
Two databases; PH2 [16] and International Skin Imaging Collaboration (ISIC) [17] database are used for performance evaluation
Summary
As the key organ guarding our internal tissues from outside intrusions, it is not rare tat skin itself is suffering from demagogical problems. Lesions are quite common on human skin. Though most lesions are risk free, skin cancer, a demagogical disease appearing like benign lesion occasionally might cause health problems. The state-of-the-art research of skin lesions classification for the identification of cancer is systematically reviewed [1]. The dermoscopic image patterns are obtained from many public databases that employ the Convolutional Neural Network (CNN). Automated classification of skin lesions using Deep Convolutional Neural Networks (DCNN) for deep feature extraction from the finegrained variable images [2]. An Artificial Intelligence (AI) with DNN is demonstrated, and the mobile devices for classifying the skin cancer to the dermatologist as an easier and low-cost classification level
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